(606e) Development of Process Operability Algorithms for Modularization and Intensification of Energy Systems

A few decades ago, the measure of process output controllability motivated the conception of the process operability approach along with its operability index (OI) ^[1] that is calculated based on input-output mappings. Recent advances on the operability framework ^[2-4] have explored the extension of the operability inputs, traditionally characterized by potential manipulated variables, to include design variables (physical dimensions, material properties, etc.). The extended approaches verified the capability of selected designs to achieve desired production and efficiency goals. In particular, process intensification (PI) and system modularization (SM) targets were considered for improved process efficiency, and an optimization-based framework was formulated to find an optimal modular design. Nevertheless, the performed calculations were restricted to nominal process conditions and thus no assessment of system operation using the OI was performed, which corresponds to a gap in the SM and PI literature.

To fill this gap, in this presentation, a newly developed multilayer operability framework ^[5] is introduced and further developed. This framework accounts for process operations by employing the measure of OI to rank competing modular designs. The framework is further developed to consider the incorporation of key operability aspects with the aim of addressing applications of different levels of complexity. Specifically, the operability input-output mapping is augmented to accommodate both design and operational variables. To obtain a modular region containing candidate designs for SM and PI, a mixed-integer linear programming algorithm is formulated based on a multimodel representation of the energy system in focus. For the multimodel representation, computational geometry tools such as Delaunay triangulation and tessellation are applied to decompose the original nonlinear mapping into a set of paired polytopes. Some of the advantages of this system representation correspond to a straightforward model inversion and fast computation of the hypervolumes for quantification of the OI.

Several input-output mapping strategies will be examined with a focus on handling increases in system dimensionality. The inclusion of process disturbances and the incorporation of flexibility and controllability will also be addressed to enhance the operability studies towards SM and PI. To show versatility of the developed framework, a variety of applications will be considered including the direct methane aromatization conversion to hydrogen and benzene, the classical shower problem in process operability, and a power plant cycling application for power generation with penetration of renewable energy sources. The resulting operability algorithms and applications will be used for construction of an open-source operability toolbox in MATLAB.

References

1. Vinson, D. R.; Georgakis, C. New Measure of Process Output Controllability. Process Control 2000, 10 (2−3), 185−194.

1. Gazzaneo, V.; Carrasco, J. C.; Lima, F. V. An MILP-Based Operability Approach for Process Intensification and Design of Modular Energy Systems. Comput.-Aided Chem. Eng. 2018, 44, 2371−2376.

3. Carrasco, J. C.; Lima, F. V. Novel Operability-Based Approach for Process Design and Intensification: Application to a Membrane Reactor for Direct Methane Aromatization. AIChE J. 2017, 63 (3), 975−983.
4. Carrasco, J. C.; Lima, F. V. Bilevel and Parallel Programing-Based Operability Approaches for Process Intensification and Modularity. AIChE J. 2018, 64 (8), 3042−3054.
5. Gazzaneo, V.; Lima, F. V. Multilayer Operability Framework for Process Design, Intensification, and Modularization of Nonlinear Energy Systems. Eng. Chem. Res. 2019, DOI: 10.1021/acs.iecr.8b05482.